doeintroduction(ppt39)(英文)-質(zhì)量工具(編輯修改稿)
2024-09-23 09:04 本頁面
【文章內(nèi)容簡介】 ve (variables data), ., temperature in degrees, time in seconds. 187。 A factor may also be qualitative (attributes data), ., different machines, different operators, clean or not clean 16 Step 2 Factor Selection Narrowing Down the List ? Which factors do we include? ? The following sources provide insight. 187。 FMEA 187。 MultiVari amp。 Hypothesis Testing 187。 Process Mapping 187。 Brainstorming 187。 Literature Review 187。 Engineering Knowledge 187。 Operator Experience 187。 Scientific Theory 187。 Customer/Supplier Input 17 Step 3 Choosing the Levels for Each Factor ? The levels of an input factor are the values of the input factor (X) being examined in the experiment (not to be confused with the output (Y)). 187。 For a quantitative (variables data) factor, . temperature: ? If an experiment is to be conducted at two different temperatures, then the factor temperature has two levels. 187。 For a qualitative (attributes data) factor, . cleanliness: ? If an experiment is to be conducted using clean and not clean, then the factor cleanliness has two levels. 18 Step 3 Choosing Input Levels Based on Objective ? Objective: Determine vital few inputs from a large number of variables (Screening) 187。 Set “Bold” levels at extremes of current capabilities 187。 Goal: if we vary the Input to extremes we will be assured of seeing an effect on the output if there is one 187。 Will exaggerate variation ? Objective: To better understand factor interactions (Mathematical Relationship) 187。 Once critical inputs are identified, reduced spacing of the levels is used to identify interactions among Inputs 187。 This approach usually leads to a series of sequential experiments ? Objective: To identify the operating window of a set of input variables (Process Optimization) 187。 Close settings are again used 187。 Sequential experimentation is also used 19 The Higher You Get, The More You Will Learn ! Step 4 Selecting the Type of Experiment Design ? Response Surface Methods ? Full Factorials with Replication ? Full Factorials with Repetition ? Full Factorials without Replication or Repetition ? Screening or Fractional Designs ? OFAT (One Factor At a Time) 20 Quantitative Output Example ? Suppose you have been watching Golf on TV and are very interested in all of the advertised items which claim to help generate improved scores by increasing the distance you drive the golf ball. You are not sure why these clubs amp。 balls will improve distance, but you are anxious for any improvement and therefore decide to purchase the following: 187。 1) Two sets of Clubs: Ping amp。 Callaway 187。 2) Two Types of Golf Balls: Dunlop amp。 Pinnacle 187。 3) You generally play at two courses and each has a different wind condition. Course A rarely has any wind as it is surrounded by mountains. Course B usually has high winds as it is located on the prairie. ? You’ve got the equipment and you’ve checked the weather. ? How do you know what to do to improve your game? 187。 Let’s try a Designed Experiment... 21 Distance (Y) is a Quantitative Data Type Recall the Strategy Step (1) ? Define the Problem: 187。 Length of drive is not long enough. ? Establish the Objective: 187
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